CN110148230A - A kind of vehicle load-carrying prediction technique based on LSTM neural network - Google Patents
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Abstract
The vehicle load-carrying prediction technique based on LSTM neural network that the present invention provides a kind of, acquires vehicle data by car-mounted terminal；Vehicle data is integrated, data cleansing and standardization, obtains training data；It is trained by shot and long term Memory Neural Networks model, obtains load-carrying model；It is predicted the load-carrying model after prediction data input training to obtain load-carrying data.A kind of vehicle load-carrying prediction technique based on LSTM neural network provided by the invention utilizes big data computing platform, application based on neural network model in prediction field, in conjunction with longitudinal dynamics vehicle mass accounting equation, it can be realized and carload in real time, accurately predict.
Description
Technical field
The vehicle load-carrying prediction technique based on LSTM neural network that the present invention relates to a kind of, belongs to car networking and logistics transportation
Technical field.
Background technique
There are mainly two types of for the method for the prediction of tradition load-carrying at present:
Method one: complete vehicle quality is estimated using forgetting factor recurrent least square method based on longitudinal dynamics.
Disadvantage: not considering gear shift operation, carried out in shift process estimation will lead to algorithm estimated accuracy it is not high and convergence speed
Degree is slow.And due to brake control power, air drag in reality, the factors real-time change such as force of rolling friction is difficult accurately to obtain,
Also the precision of prediction is affected to a certain extent.Its prediction result and true value comparison error range be difficult control 15% with
It is interior.
Method two: using sensor is installed on automobile, vehicle live load is estimated by sensor deformation.
Disadvantage: it requires that sensor is additionally installed on every trolley, the price of a common vehicle load sensor just exists
It is 10000 yuans or more, with high costs, and the sensor based on deformation is easy aging, will increase later maintenance cost, it is right
The calculating of load-carrying precision can also have an impact.
Summary of the invention
In order to solve the deficiencies in the prior art, increases predictablity rate, reduce cost, the present invention provides one kind to be based on
The vehicle load-carrying prediction technique of LSTM neural network, using big data computing platform, based on neural network model in prediction field
Application, can in the case where additionally not adding vehicle load sensor in conjunction with longitudinal dynamics vehicle mass accounting equation
It realizes and carload in real time, accurately predict, prediction result error is within 5%.
The present invention is that technical solution used by solving its technical problem is: being provided a kind of based on LSTM neural network
Vehicle load-carrying prediction technique, comprising the following steps:
(1) vehicle data is acquired by car-mounted terminal；
(2) vehicle data is integrated, data cleansing and standardization, obtains training data；
(3) training data is trained by shot and long term Memory Neural Networks model, obtains load-carrying model；
(4) the load-carrying model after prediction data input training is predicted, obtains the load-carrying prediction result of target vehicle
Collection, takes the median of result set as final prediction result.
Step (1) described vehicle data include the moment, speed, engine speed, clutch switch, gas pedal aperture and
Brake switch and torque or torque percentage.
Step (2) integration is that every vehicle data at continuous two moment is integrated into one group of characteristic index number respectively
According to.
Step (2) described data cleansing is to be filtered according to the following conditions to vehicle data, for the vehicle at each moment
Data, meet following all conditions and just retain:
Condition one: the difference of the speed of later moment in time and previous moment is greater than 0；
Condition two: the effective range of engine speed n is 700 revs/min of 2500 revs/min of < n <；
Condition three: the effective range of torque T e is 1000Nm < Te < 2200Nm；
Condition four: brake switch fbswitch=0；
Condition five: time interval deltaT≤5s of later moment in time and previous moment；
Condition six: Isolating Switch lhswitch=0.
Step (2) is standardized using z-score standardized method.
Step (3) is during shot and long term Memory Neural Networks model is trained, using RMSE as evaluation criterion,
Wherein training set error loss train_loss, verifying collection error loss val_loss and prediction and error loss pre_loss are got over
It is small, indicate that the fitting effect of shot and long term Memory Neural Networks model is better.
For load-carrying model, the Outside Access interface of load-carrying model is set, is called for third party and is predicted to obtain load-carrying
Data.
For load-carrying model, it is transplanted to car-mounted terminal, for collected vehicle data directly to be inputted load-carrying mould
Type, the load-carrying data that output prediction obtains.
The present invention is based on beneficial effects possessed by its technical solution to be:
(1) a kind of vehicle load-carrying prediction technique calculating process based on LSTM neural network provided by the invention does not depend on brake
Vehicle brake force, air drag, force of rolling friction etc. be difficult to acquire with environmental factor delta data, by experiment, prediction result
Error is within 5%, and compared with traditional least square method error range is more than 15%, precision of prediction is obviously improved；
(2) a kind of vehicle load-carrying prediction technique based on LSTM neural network provided by the invention only acquires car-mounted terminal hair
The data sent save manufacturing cost and later maintenance cost without additionally installing the relevant sensor of any quality；
(3) a kind of vehicle load-carrying prediction technique based on LSTM neural network provided by the invention can be to vehicle in terms of transport
Carry out Payload Monitoring And Control during transportation, rationally utilize (scheduling) load resource, monitor cargo whether reach designated place with
And early warning is carried out to overload, overload；
(4) can there is aspect after a kind of vehicle load-carrying prediction technique based on LSTM neural network provided by the invention is on sale
Effect, which understands the failure defining problem that vehicle is under warranty and overload, influences vehicle component service life bring；Whole
Vehicle research and development aspect, it is whole to improve by performance and fuel-economy sex expression of the big data mining analysis vehicle under different loads
Vehicle level provides data foundation；
(5) it is provided by the invention it is a kind of based on the vehicle load-carrying prediction technique of LSTM neural network in vehicle performance optimization side
Face can pass through the prediction of real-time load and interact result and entire car controller, and it is excellent to reach controller control strategy dynamic
Change, improves vehicle economical operation benefit and safety.
Detailed description of the invention
Fig. 1 is the schematic diagram data after integration.
Specific embodiment
Present invention will be further explained below with reference to the attached drawings and examples.
The vehicle load-carrying prediction technique based on LSTM neural network that the present invention provides a kind of, comprising the following steps:
(1) vehicle data is acquired by car-mounted terminal (T-BOX), including moment, speed, engine speed, clutch open
Pass, gas pedal aperture and brake switch and torque or torque percentage；
(2) vehicle data is integrated, data cleansing and standardization, obtains training data；
This step is mainly prepared work to data.By longitudinal dynamics formula:
Wherein M indicates the total quality of automobile, and v indicates automobile driving speed (can acquire data), accelerationTeTable
Show torque (can acquire data) of the engine action on flywheel,Indicate total gear ratio, wherein rwIndicate wheel half
Diameter (vehicle quiescent value), gdFor gear ratio (vehicle quiescent value), gfIndicate driving than (vehicle quiescent value), JeIndicate rotary inertia
(vehicle quiescent value), ω indicate engine speed (can acquire data), FfbIndicate that the braking friction on wheel (can acquire number
According to),Wherein CdIt is coefficient of air resistance, ρ indicates atmospheric density, and A is automotive windshield area, Fgrade=
Mg (μ cos β+sin β) is the resultant force generated by the gradient and rolling friction, and wherein μ indicates that road surface rolling friction force coefficient, β indicate
The gradient, β>0 are to go up a slope, and β<0 is descending, and β=0 is no gradient.
By longitudinal dynamics formula it is found that terminal acquisition data in, information relevant to quality has: the time, speed,
Transmitter revolving speed, torque, brake switch, clutch switch and gas pedal aperture.Since the calculating of acceleration at least needs two
The reported data at a moment, therefore data are integrated first, every vehicle data at continuous two moment is integrated into respectively
One group of characteristic index data, i.e., every data line shown in FIG. 1.
Then data cleansing is carried out, some wrong data are filtered out and generates the dirty data of interference to calculated result.According to
The following conditions are filtered vehicle data, for the vehicle data at each moment, meet following all conditions and just retain:
Condition one: the difference of the speed of later moment in time and previous moment is greater than 0；
Condition two: the effective range of engine speed n is 700 revs/min of 2500 revs/min of < n <；
Condition three: the effective range of torque T e is 1000Nm < Te < 2200Nm；
Condition four: brake switch fbswitch=0；
Condition five: time interval deltaT≤5s of later moment in time and previous moment；
Condition six: Isolating Switch lhswitch=0.
The data of acquisition include multiple indexs, since the property of each index is different, have different dimension and the order of magnitude.It examines
The level difference considered between certain indexs is larger, if directly analyzed with original index value, it is higher will to protrude numerical value
Effect of the index in comprehensive analysis, the opposite effect for weakening the horizontal lower index of numerical value.Therefore, in order to guarantee the reliable of result
Property, it needs to be standardized original index data.Standard is carried out using data of the z-score standardized method to acquisition
Change processing, mean value (mean) and standard deviation (standard deviation) of this method based on initial data carry out data
Standardization, i.e. new data=(former data-mean value)/standard deviation.
(3) shot and long term Memory Neural Networks model is established, input layer number is 8, and output layer neuron number is
1, activation primitive ReLU, mathematic(al) representation are as follows: f (x)=max (α x, x), wherein α=0.01, x are input vector, f (x)
For output vector, activation primitive is a non-linear transfer function of neural computing pilot process, for realizing one-to-one
Transformation, i.e., mapped with each component of the identical function to input vector, obtain output vector.Optimizer uses
Adam.Training data is trained by shot and long term Memory Neural Networks model, obtains load-carrying model.To make prediction result more
It is accurate to add, and reduces influence of the item forecast result to whole prediction result, when being predicted, prediction data takes continuous time period
Interior data set (prediction data is no less than 50), is entered into load-carrying model and obtains prediction result collection, take in result set
Digit is as final prediction result and output.
In training process, evaluation criterion is used as using RMSE (Root Mean Squared Error, root-mean-square error),
Parameter optimization is also to carry out on the basis of RMSE, and the loss function etc. of load-carrying prediction is also all on the basis of RMSE
It is iterated optimization, error costing bio disturbance formula are as follows:
Wherein tiIndicate target value (preset value) corresponding to i-th group of characteristic index data of input, yiIndicate i-th group of input
Predicted value corresponding to characteristic index data (result of output) obtains corresponding error by the difference of input data set and damages
It loses.Training set error is obtained when input data set is training set loses train_loss；When input data is verifying collection, obtain
Val_loss is lost to verifying collection error；When input data set is forecast set, forecast set error loss pre_loss is obtained.Respectively
The value of loss is lower, and the effect for illustrating models fitting is better.
(4) in view of prediction result concentration has that individual data is serious bigger than normal or less than normal, in order to preferably show
Load-carrying model after prediction data input training is predicted, obtains target vehicle by the mean level of this group of prediction result
Load-carrying prediction result collection exports final prediction result using the method for taking median to result set, and the computation rule of median is such as
Under:
The data concentrated for prediction result: X1..., XN, arranged by sequence from small to large are as follows: X(1)...,
X(N).When N is odd number, load value m is predicted0.5=X(N+1)/2, when N is even number, predict load value
For load-carrying model, load-carrying model is arranged is externally based on ICP/IP protocol stack addressing interface, calls for third party
It is predicted to obtain load-carrying data.By interface, predictive data set is uploaded, selects prediction data characteristic series, characteristic series must wrap
Have containing information: calling time in data, speed, engine speed, clutch switch, gas pedal aperture, brake switch, torque or
Person's torque percentage.Then it calls and has saved prediction model, finally obtain prediction result.
For load-carrying model, it can also be transplanted to car-mounted terminal (T-BOX), by T-BOX, acquire predictive data set,
Prediction data characteristic series are selected, characteristic series must have comprising information: call time in data, speed, engine speed, clutch are opened
It closes, gas pedal aperture, brake switch, collected vehicle data is directly inputted load-carrying mould by torque or torque percentage
Type, the load-carrying data finally predicted, and the interaction of direct and vehicle control device, realize optimizing entire vehicle controller strategy.
A kind of vehicle load-carrying prediction technique based on LSTM neural network provided by the invention, by neural network model with
Longitudinal dynamics combine, and longitudinal dynamics provide theories integration for selection, the integration of neural network model feature column data,
LSTM network provides the machine learning algorithm with memory function, and the data reported using car-mounted terminal no longer need to pass by other
Sensor is enough quick and precisely to identify vehicle load.
Claims (8)
1. a kind of vehicle load-carrying prediction technique based on LSTM neural network, it is characterised in that the following steps are included:
(1) vehicle data is acquired by car-mounted terminal；
(2) vehicle data is integrated, data cleansing and standardization, obtains training data；
(3) training data is trained by shot and long term Memory Neural Networks model, obtains load-carrying model；
(4) the load-carrying model after prediction data input training is predicted, obtains the load-carrying prediction result collection of target vehicle, takes
The median of result set is as final prediction result.
2. the vehicle load-carrying prediction technique according to claim 1 based on LSTM neural network, it is characterised in that: step
(1) vehicle data includes moment, speed, engine speed, clutch switch, gas pedal aperture and brake switch, with
And torque or torque percentage.
3. the vehicle load-carrying prediction technique according to claim 1 based on LSTM neural network, it is characterised in that: step
(2) integration is that every vehicle data at continuous two moment is integrated into one group of characteristic index data respectively.
4. the vehicle load-carrying prediction technique according to claim 1 based on LSTM neural network, it is characterised in that: step
(2) data cleansing is to be filtered according to the following conditions to vehicle data, for the vehicle data at each moment, meet with
Lower all conditions just retain:
Condition one: the difference of the speed of later moment in time and previous moment is greater than 0；
Condition two: the effective range of engine speed n is 700 revs/min of 2500 revs/min of < n <；
Condition three: the effective range of torque T e is 1000Nm < Te < 2200Nm；
Condition four: brake switch fbswitch=0；
Condition five: time interval deltaT≤5s of later moment in time and previous moment；
Condition six: Isolating Switch lhswitch=0.
5. the vehicle load-carrying prediction technique according to claim 1 based on LSTM neural network, it is characterised in that: step
(2) it is standardized using z-score standardized method.
6. the vehicle load-carrying prediction technique according to claim 1 based on LSTM neural network, it is characterised in that: step
(3) during shot and long term Memory Neural Networks model is trained, using RMSE as evaluation criterion, wherein training set is missed
Differential loss loses train_loss, verifying collection error loss val_loss and prediction and error loss pre_loss is smaller, indicates length
The phase fitting effect of Memory Neural Networks model is better.
7. the vehicle load-carrying prediction technique according to claim 1 based on LSTM neural network, it is characterised in that: for carrying
The Outside Access interface of load-carrying model is arranged in molality type, calls for third party and is predicted to obtain load-carrying data.
8. the vehicle load-carrying prediction technique according to claim 1 based on LSTM neural network, it is characterised in that: for carrying
Molality type, is transplanted to car-mounted terminal, and for collected vehicle data directly to be inputted load-carrying model, output prediction is obtained
Load-carrying data.
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